stThreat)Geiser C. Challco geiser@alumni.usp.br
cond <- "stThreat"
to_remove <- c('S11')
sub.groups <- c("country","age","ed.level","intervention",
"country:age","country:ed.level","country:intervention",
"age:intervention","ed.level:intervention",
"country:age:intervention","country:ed.level:intervention")dat <- read_excel("../data/data-without-outliers.xlsx", sheet = "perform-cond-descriptive")
dat <- dat[!dat$study %in% to_remove, ]
leg <- read_excel("../data/data-without-outliers.xlsx", sheet = "legend")## New names:
## • `` -> `...10`leg <- leg[!leg$study %in% to_remove, ]
idx.e <- which(dat$condition==cond)
idx.c <- which(dat$condition=="control")
data <- data.frame(
study = dat$study[idx.c],
n.e = dat$N[idx.e], mean.e = dat$M[idx.e], sd.e = dat$SD[idx.e],
n.c = dat$N[idx.c], mean.c = dat$M[idx.c], sd.c = dat$SD[idx.c]
)
for (cgroups in strsplit(sub.groups,":")) {
data[[paste0(cgroups, collapse = ":")]] <- sapply(data$study, FUN = function(x) {
paste0(sapply(cgroups, FUN = function(namecol) leg[[namecol]][which(x == leg$study)]), collapse = ":")
})
}
data[["lbl"]] <- sapply(data$study, FUN = function(x) leg$Note[which(x == leg$study)])m.cont <- metacont(
n.e = n.e, mean.e = mean.e, sd.e = sd.e, n.c = n.c, mean.c = mean.c, sd.c = sd.c,
studlab = lbl, data = data, sm = "SMD", method.smd = "Hedges",
fixed = F, random = T, method.tau = "REML", hakn = T, title = paste("Performance in",cond)
)
summary(m.cont)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 -0.3302 [-0.8631; 0.2026] 8.2
## S2 0.0653 [-0.3468; 0.4773] 11.8
## S3 -0.4150 [-0.9402; 0.1103] 8.4
## S4 -0.0924 [-0.6256; 0.4408] 8.2
## S5 0.1959 [-0.2701; 0.6619] 10.0
## S6 0.2959 [-0.1777; 0.7695] 9.8
## S7 0.2003 [-0.2012; 0.6017] 12.2
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.cont, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = country, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random) country
## S1 -0.3302 [-0.8631; 0.2026] 8.2 Brazil
## S2 0.0653 [-0.3468; 0.4773] 11.8 Brazil
## S3 -0.4150 [-0.9402; 0.1103] 8.4 Brazil
## S4 -0.0924 [-0.6256; 0.4408] 8.2 Brazil
## S5 0.1959 [-0.2701; 0.6619] 10.0 Brazil
## S6 0.2959 [-0.1777; 0.7695] 9.8 Brazil
## S7 0.2003 [-0.2012; 0.6017] 12.2 Brazil
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2 China
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9 Brazil
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3 Brazil
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country = Brazil 9 -0.0667 [-0.2873; 0.1539] 0.0272 0.1650 11.85 32.5%
## country = China 1 0.1809 [-0.3141; 0.6759] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.84 1 0.3592
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = age, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random) age
## S1 -0.3302 [-0.8631; 0.2026] 8.2 adolescent
## S2 0.0653 [-0.3468; 0.4773] 11.8 adolescent
## S3 -0.4150 [-0.9402; 0.1103] 8.4 adolescent
## S4 -0.0924 [-0.6256; 0.4408] 8.2 adult
## S5 0.1959 [-0.2701; 0.6619] 10.0 adult
## S6 0.2959 [-0.1777; 0.7695] 9.8 adult
## S7 0.2003 [-0.2012; 0.6017] 12.2 adult
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2 unknown
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9 no-restriction
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3 adolescent
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## age = adolescent 4 -0.1725 [-0.5126; 0.1676] 0 0 2.44 0.0%
## age = adult 4 0.1671 [-0.0733; 0.4075] 0 0 1.24 0.0%
## age = unknown 1 0.1809 [-0.3141; 0.6759] -- -- 0.00 --
## age = no-restriction 1 -0.4832 [-0.9228; -0.0436] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 12.47 3 0.0059
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = ed.level, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random) ed.level
## S1 -0.3302 [-0.8631; 0.2026] 8.2 upper-secundary
## S2 0.0653 [-0.3468; 0.4773] 11.8 upper-secundary
## S3 -0.4150 [-0.9402; 0.1103] 8.4 upper-secundary
## S4 -0.0924 [-0.6256; 0.4408] 8.2 higher-education
## S5 0.1959 [-0.2701; 0.6619] 10.0 higher-education
## S6 0.2959 [-0.1777; 0.7695] 9.8 higher-education
## S7 0.2003 [-0.2012; 0.6017] 12.2 unknown
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2 unknown
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9 unknown
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3 upper-secundary
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## ed.level = upper-secundary 4 -0.1725 [-0.5126; 0.1676] 0 0 2.44 0.0%
## ed.level = higher-education 3 0.1507 [-0.3279; 0.6294] 0 0 1.20 0.0%
## ed.level = unknown 3 -0.0353 [-1.0048; 0.9342] 0.1022 0.3197 6.04 66.9%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 4.40 2 0.1110
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 -0.3302 [-0.8631; 0.2026] 8.2
## S2 0.0653 [-0.3468; 0.4773] 11.8
## S3 -0.4150 [-0.9402; 0.1103] 8.4
## S4 -0.0924 [-0.6256; 0.4408] 8.2
## S5 0.1959 [-0.2701; 0.6619] 10.0
## S6 0.2959 [-0.1777; 0.7695] 9.8
## S7 0.2003 [-0.2012; 0.6017] 12.2
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3
## intervention
## S1 Gender-stereotype color, ranking, badges, and avatar
## S2 Gender-stereotype color, ranking, badges, and avatar
## S3 Gender-stereotype color, ranking, badges, and avatar
## S4 Gender-stereotype color, ranking, badges, and avatar
## S5 Gender-stereotype color, ranking, badges, and avatar
## S6 Gender-stereotype color, ranking, badges, and avatar
## S7 Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## intervention = Gender-stereotype color, rankin ... 9 -0.0290 [-0.2575; 0.1994] 0.0313 0.1768 12.30 34.9%
## intervention = Gender-stereotyped motivational ... 1 -0.1663 [-0.5939; 0.2614] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 0.33 1 0.5668
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `country:age`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random) country:age
## S1 -0.3302 [-0.8631; 0.2026] 8.2 Brazil:adolescent
## S2 0.0653 [-0.3468; 0.4773] 11.8 Brazil:adolescent
## S3 -0.4150 [-0.9402; 0.1103] 8.4 Brazil:adolescent
## S4 -0.0924 [-0.6256; 0.4408] 8.2 Brazil:adult
## S5 0.1959 [-0.2701; 0.6619] 10.0 Brazil:adult
## S6 0.2959 [-0.1777; 0.7695] 9.8 Brazil:adult
## S7 0.2003 [-0.2012; 0.6017] 12.2 Brazil:adult
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2 China:unknown
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9 Brazil:no-restriction
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3 Brazil:adolescent
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country:age = Brazil:adolescent 4 -0.1725 [-0.5126; 0.1676] 0 0 2.44 0.0%
## country:age = Brazil:adult 4 0.1671 [-0.0733; 0.4075] 0 0 1.24 0.0%
## country:age = China:unknown 1 0.1809 [-0.3141; 0.6759] -- -- 0.00 --
## country:age = Brazil:no-restriction 1 -0.4832 [-0.9228; -0.0436] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 12.47 3 0.0059
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random) country:ed.level
## S1 -0.3302 [-0.8631; 0.2026] 8.2 Brazil:upper-secundary
## S2 0.0653 [-0.3468; 0.4773] 11.8 Brazil:upper-secundary
## S3 -0.4150 [-0.9402; 0.1103] 8.4 Brazil:upper-secundary
## S4 -0.0924 [-0.6256; 0.4408] 8.2 Brazil:higher-education
## S5 0.1959 [-0.2701; 0.6619] 10.0 Brazil:higher-education
## S6 0.2959 [-0.1777; 0.7695] 9.8 Brazil:higher-education
## S7 0.2003 [-0.2012; 0.6017] 12.2 Brazil:unknown
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2 China:unknown
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9 Brazil:unknown
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3 Brazil:upper-secundary
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## country:ed.level = Brazil:upper-secundary 4 -0.1725 [-0.5126; 0.1676] 0 0 2.44 0.0%
## country:ed.level = Brazil:higher-education 3 0.1507 [-0.3279; 0.6294] 0 0 1.20 0.0%
## country:ed.level = Brazil:unknown 2 -0.1353 [-4.4768; 4.2061] 0.1874 0.4329 5.06 80.3%
## country:ed.level = China:unknown 1 0.1809 [-0.3141; 0.6759] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 5.10 3 0.1643
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `country:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 -0.3302 [-0.8631; 0.2026] 8.2
## S2 0.0653 [-0.3468; 0.4773] 11.8
## S3 -0.4150 [-0.9402; 0.1103] 8.4
## S4 -0.0924 [-0.6256; 0.4408] 8.2
## S5 0.1959 [-0.2701; 0.6619] 10.0
## S6 0.2959 [-0.1777; 0.7695] 9.8
## S7 0.2003 [-0.2012; 0.6017] 12.2
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3
## country:intervention
## S1 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## country:intervention = Brazil:Gender-stereotype color, ... 8 -0.0546 [-0.3105; 0.2013] 0.0377 0.1942
## country:intervention = China:Gender-stereotype color, ... 1 0.1809 [-0.3141; 0.6759] -- --
## country:intervention = Brazil:Gender-stereotyped motiv ... 1 -0.1663 [-0.5939; 0.2614] -- --
## Q I^2
## country:intervention = Brazil:Gender-stereotype color, ... 11.58 39.6%
## country:intervention = China:Gender-stereotype color, ... 0.00 --
## country:intervention = Brazil:Gender-stereotyped motiv ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 1.12 2 0.5722
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `age:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 -0.3302 [-0.8631; 0.2026] 8.2
## S2 0.0653 [-0.3468; 0.4773] 11.8
## S3 -0.4150 [-0.9402; 0.1103] 8.4
## S4 -0.0924 [-0.6256; 0.4408] 8.2
## S5 0.1959 [-0.2701; 0.6619] 10.0
## S6 0.2959 [-0.1777; 0.7695] 9.8
## S7 0.2003 [-0.2012; 0.6017] 12.2
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3
## age:intervention
## S1 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4 adult:Gender-stereotype color, ranking, badges, and avatar
## S5 adult:Gender-stereotype color, ranking, badges, and avatar
## S6 adult:Gender-stereotype color, ranking, badges, and avatar
## S7 adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs adolescent:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q
## age:intervention = adolescent:Gender-stereotype co ... 3 -0.1887 [-0.8543; 0.4770] 0.0191 0.1382 2.43
## age:intervention = adult:Gender-stereotype color, ... 4 0.1671 [-0.0733; 0.4075] 0 0 1.24
## age:intervention = unknown:Gender-stereotype color ... 1 0.1809 [-0.3141; 0.6759] -- -- 0.00
## age:intervention = no-restriction:Gender-stereotyp ... 1 -0.4832 [-0.9228; -0.0436] -- -- 0.00
## age:intervention = adolescent:Gender-stereotyped m ... 1 -0.1663 [-0.5939; 0.2614] -- -- 0.00
## I^2
## age:intervention = adolescent:Gender-stereotype co ... 17.8%
## age:intervention = adult:Gender-stereotype color, ... 0.0%
## age:intervention = unknown:Gender-stereotype color ... --
## age:intervention = no-restriction:Gender-stereotyp ... --
## age:intervention = adolescent:Gender-stereotyped m ... --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 11.66 4 0.0200
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 -0.3302 [-0.8631; 0.2026] 8.2
## S2 0.0653 [-0.3468; 0.4773] 11.8
## S3 -0.4150 [-0.9402; 0.1103] 8.4
## S4 -0.0924 [-0.6256; 0.4408] 8.2
## S5 0.1959 [-0.2701; 0.6619] 10.0
## S6 0.2959 [-0.1777; 0.7695] 9.8
## S7 0.2003 [-0.2012; 0.6017] 12.2
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3
## ed.level:intervention
## S1 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7 unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs upper-secundary:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## ed.level:intervention = upper-secundary:Gender-stereoty ... 3 -0.1887 [-0.8543; 0.4770] 0.0191 0.1382
## ed.level:intervention = higher-education:Gender-stereot ... 3 0.1507 [-0.3279; 0.6294] 0 0
## ed.level:intervention = unknown:Gender-stereotype color ... 3 -0.0353 [-1.0048; 0.9342] 0.1022 0.3197
## ed.level:intervention = upper-secundary:Gender-stereoty ... 1 -0.1663 [-0.5939; 0.2614] -- --
## Q I^2
## ed.level:intervention = upper-secundary:Gender-stereoty ... 2.43 17.8%
## ed.level:intervention = higher-education:Gender-stereot ... 1.20 0.0%
## ed.level:intervention = unknown:Gender-stereotype color ... 6.04 66.9%
## ed.level:intervention = upper-secundary:Gender-stereoty ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 3.93 3 0.2693
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `country:age:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 -0.3302 [-0.8631; 0.2026] 8.2
## S2 0.0653 [-0.3468; 0.4773] 11.8
## S3 -0.4150 [-0.9402; 0.1103] 8.4
## S4 -0.0924 [-0.6256; 0.4408] 8.2
## S5 0.1959 [-0.2701; 0.6619] 10.0
## S6 0.2959 [-0.1777; 0.7695] 9.8
## S7 0.2003 [-0.2012; 0.6017] 12.2
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3
## country:age:intervention
## S1 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:adult:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:no-restriction:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:adolescent:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 3 -0.1887 [-0.8543; 0.4770] 0.0191 0.1382
## country:age:intervention = Brazil:adult:Gender-stereotype ... 4 0.1671 [-0.0733; 0.4075] 0 0
## country:age:intervention = China:unknown:Gender-stereotype ... 1 0.1809 [-0.3141; 0.6759] -- --
## country:age:intervention = Brazil:no-restriction:Gender-st ... 1 -0.4832 [-0.9228; -0.0436] -- --
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 1 -0.1663 [-0.5939; 0.2614] -- --
## Q I^2
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 2.43 17.8%
## country:age:intervention = Brazil:adult:Gender-stereotype ... 1.24 0.0%
## country:age:intervention = China:unknown:Gender-stereotype ... 0.00 --
## country:age:intervention = Brazil:no-restriction:Gender-st ... 0.00 --
## country:age:intervention = Brazil:adolescent:Gender-stereo ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 11.66 4 0.0200
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `country:ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 -0.3302 [-0.8631; 0.2026] 8.2
## S2 0.0653 [-0.3468; 0.4773] 11.8
## S3 -0.4150 [-0.9402; 0.1103] 8.4
## S4 -0.0924 [-0.6256; 0.4408] 8.2
## S5 0.1959 [-0.2701; 0.6619] 10.0
## S6 0.2959 [-0.1777; 0.7695] 9.8
## S7 0.2003 [-0.2012; 0.6017] 12.2
## S8: Conducted by BNU 0.1809 [-0.3141; 0.6759] 9.2
## S9: Albuquerque, et al. (2017) -0.4832 [-0.9228; -0.0436] 10.9
## S10: Only use prompt msgs -0.1663 [-0.5939; 0.2614] 11.3
## country:ed.level:intervention
## S1 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3 Brazil:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6 Brazil:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7 Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S8: Conducted by BNU China:unknown:Gender-stereotype color, ranking, badges, and avatar
## S9: Albuquerque, et al. (2017) Brazil:unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Brazil:upper-secundary:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 10
## Number of observations: o = 741
##
## SMD 95%-CI t p-value
## Random effects model -0.0434 [-0.2450; 0.1583] -0.49 0.6382
##
## Quantifying heterogeneity:
## tau^2 = 0.0233 [0.0000; 0.2097]; tau = 0.1525 [0.0000; 0.4579]
## I^2 = 29.0% [0.0%; 66.0%]; H = 1.19 [1.00; 1.72]
##
## Test of heterogeneity:
## Q d.f. p-value
## 12.68 9 0.1775
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 3 -0.1887 [-0.8543; 0.4770] 0.0191
## country:ed.level:intervention = Brazil:higher-education:Gender- ... 3 0.1507 [-0.3279; 0.6294] 0
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ... 2 -0.1353 [-4.4768; 4.2061] 0.1874
## country:ed.level:intervention = China:unknown:Gender-stereotype ... 1 0.1809 [-0.3141; 0.6759] --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 1 -0.1663 [-0.5939; 0.2614] --
## tau Q I^2
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... 0.1382 2.43 17.8%
## country:ed.level:intervention = Brazil:higher-education:Gender- ... 0 1.20 0.0%
## country:ed.level:intervention = Brazil:unknown:Gender-stereotyp ... 0.4329 5.06 80.3%
## country:ed.level:intervention = China:unknown:Gender-stereotype ... -- 0.00 --
## country:ed.level:intervention = Brazil:upper-secundary:Gender-s ... -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 4.54 4 0.3381
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.cont <- update.meta(m.cont, studlab = data$study)
summary(eggers.test(x = m.cont))## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## -3.205 -10.62 - 4.21 -0.847 0.42
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.funnel(m.cont, xlab = "Hedges' g", studlab = T, legend=T, addtau2 = T)